Integrating nature-inspired optimization algorithms to K-means clustering

@article{Tang2012IntegratingNO,
  title={Integrating nature-inspired optimization algorithms to K-means clustering},
  author={Rui Tang and Simon Fong and Xin-She Yang and Suash Deb},
  journal={Seventh International Conference on Digital Information Management (ICDIM 2012)},
  year={2012},
  pages={116-123}
}
Although K-means clustering algorithm is simple and popular, it has a fundamental drawback of falling into local optima that depend on the randomly generated initial centroid values. Optimization algorithms are well known for their ability to guide iterative computation in searching for global optima. They also speed up the clustering process by achieving early convergence. Contemporary optimization algorithms inspired by biology, including the Wolf, Firefly, Cuckoo, Bat and Ant algorithms… CONTINUE READING
Highly Cited
This paper has 54 citations. REVIEW CITATIONS